Multi-agent Simulation Research Articles

Facing Climate Change: How to Manage the Arising New Crises?

The increasing frequency and intensity of climate change-related disasters highlight the need to develop adaptive crisis management approaches. This paper explores the integration of R-IO Suite, a knowledge-based decision support system, with GAMA, a multi-agent simulation platform, to improve situational awareness and crisis response in a changing climate. This approach enables decision-makers and local communities to better understand, anticipate, and mitigate crisis impacts by combining real-time data modelling and predictive simulation. Although the proposed framework has been conceptually defined, its implementation and validation remain open key challenges. The French ANR-funded ATEsT project aims to overcome major technical obstacles— interoperability, semantic alignment, and temporal synchronisation—through concrete use cases. Future work will focus on implementing and testing this integration in realistic crisis scenarios, such as mega-fires and flash floods across different territories, to assess its effectiveness in improving decision-making processes and citizen engagement.

Multi-agent simulation of team stability evolution: A complexity science perspective

Multi-agent simulation of team stability evolution: A complexity science perspective

Multi-agent simulation of policies driving CCS technology in the cement industry

Multi-agent simulation of policies driving CCS technology in the cement industry

A discrete-event multi-agent simulation framework supporting well-to-wheel analysis for greening commercial maritime ports

A discrete-event multi-agent simulation framework supporting well-to-wheel analysis for greening commercial maritime ports

Analysis of collusive bidding characteristics in the power spot market based on multi-agent reinforcement learning simulation

Analysis of collusive bidding characteristics in the power spot market based on multi-agent reinforcement learning simulation

Investigating dominant situation of resting behavior as a potential labor pool in robotic swarm for group foraging

The foraging task is a commonly studied scenario for distributed swarm robotic systems. The robots switch searching and resting behavior in a distributed manner to perform foraging quickly and energy efficiently. It is known that robots that are resting can act as a potential labor pool in addition to saving energy. Because they function as a potential labor pool, resting robots can help with foraging if needed. In this study, we consider the group foraging task, which requires two or more robots to transport a food item. In a group foraging task, resting robots can help with the transportation of a heavy food item in response to the recruitment of other robots. Until now, the efficacy of the resting behavior as a potential labor pool has been suggested, but the environments in which this function is dominant is still unknown. In this study, we propose a state-transition model for robots that includes the resting behavior and investigate the performance through multi-agent simulation. By comparing models with and without the resting behavior, we found that the function of the resting behavior as a potential labor pool is dominant in cases when the food items are heavy or the population is small.

Resilience of Technician Allocation in Multi-Agent Simulations

Resilience of Technician Allocation in Multi-Agent Simulations

Black-box vs. White-box Models, Integrated vs. Cascade Simulations, and Multi-agent Simulation vs. Computational Fluid Dynamics

Black-box vs. White-box Models, Integrated vs. Cascade Simulations, and Multi-agent Simulation vs. Computational Fluid Dynamics

Dynamic charging strategies for battery-powered IGV based on multi-agent simulation in automated container terminals

Dynamic charging strategies for battery-powered IGV based on multi-agent simulation in automated container terminals

Refining arrival headway for high-speed trains approaching a large railway station: a speed profile intervention approach

Abstract Arrival headway, the minimum time interval between two trains that consecutively stop in the same railway yard, significantly influences overall railway capacity and becomes a bottleneck in large high-speed railway yards. This occurs because the leading train slows considerably along the long receiving route to the yard; while, the following train continues at top speed, creating a substantial spatial and temporal interval between them. This paper proposes a speed profile intervention (SPI) approach to reduce arrival headway. By setting appropriate speed limits in specific block sections for each train, the following train decelerates in advance, thereby shortening the interval with its predecessor. We first study the impact of speed values and locations on arrival headway theoretically, then validate our findings through a multi-agent simulation approach to quantitatively investigate the relationship between headway and SPI parameters. Simulation experiments using real-world data from the Beijing–Shanghai high-speed railway demonstrate that the SPI approach can significantly reduce arrival headway without any infrastructure modifications. To mitigate potential side effects on travel time associated with this approach, we perform an analysis that involves setting appropriate speed limit values and selecting strategic locations for their implementation.

Agent-based modelling of older adult needs for autonomous mobility-on-demand: a case study in Winnipeg, Canada

Abstract As the populations continue to age across many nations, ensuring accessible and efficient transportation options for older adults has become an increasingly important concern. Autonomous Mobility-on-Demand (AMoD) systems have emerged as a potential solution to address the needs faced by older adults in their daily mobility. However, estimation of older adult mobility needs, and how they vary over space and time, is crucial for effective planning and implementation of such service, and conventional four-step approaches lack the granularity to fully account for these needs. To address this challenge, we propose an agent-based model of older adults mobility demand in Winnipeg, Canada. The model is built for 2022 using primarily open data, and is implemented in the Multi-Agent Transport Simulation toolkit. After calibration to accurately reproduce observed travel behaviours, a new AMoD service is tested in simulation and its potential adoption among Winnipeg older adults is explored. The model can help policy makers to estimate the needs of the elderly populations for door-to-door transportation and can guide the design of AMoD transport systems.

High-fidelity Multi-agent Simulations for Social Systems

High-fidelity Multi-agent Simulations for Social Systems

Emergence of echo chambers in social media communication involving multiple topics

ABSTRACT In recent years, research on the existence and impact of echo chambers has proposed mixed perspectives. Several studies have pointed out that actual social media networks are segregated based on users’ preferences regarding specific topics, limiting opportunities for exposure to opposing opinions and thus suggesting the formation of echo chambers. On the other hand, several studies suggest that echo chambers are overstated, as the followers of mainstream media provided opportunities to be exposed to information that contradicts their own preferences. We assumed that these conflicting perspectives arise from variations in the diversity of topics in their research area. Therefore, we examined how the increase in topics within communication spaces on social media affects the formation of echo chambers. Experiments based on Multi-Agent Simulations reveal that echo chambers were formed when the number of topics was small, but as the number of topics increased, the formation of echo chambers was inhibited. However, in situations involving multiple topics, it is improbable that individuals will address all topics equally. To address this, we applied the concept of agenda-setting effects, enabling agents to recognize topics with more exposure as important and adding conditions to assign weights to those topics in additional experiments. As a result, the network of agents became echo chambers regarding topics recognized as important by most agents. From these results, our study suggests that the contradictory perspectives regarding the existence of echo chambers in related research can be reconciled by considering the ‘number of topics’ and ‘the weights of topics.’

Simulation of Autonomous Industrial Vehicle Fleet Using Fuzzy Agents: Application to Task Allocation and Battery Charge Management

The research introduces a multi-agent simulation that uses fuzzy inference to investigate the work distribution and battery charging control of mobile baggage conveyor robots in an airport in a comprehensive manner. Thanks to a distributed system, this simulation approach provides high adaptability, adjusting to changes in conveyor agent availability, battery capacity, awareness of the activities of the conveyor fleet, and knowledge of the context of infrastructure resource availability. Dynamic factors, such as workload variations and communication between the conveyor agents and infrastructure are considered as heuristics, highlighting the importance of flexible and collaborative approaches in autonomous systems. The results highlight the effectiveness of adaptive fuzzy multi-agent models to optimize dynamic task allocation, adapt to the variation of baggage arrival flows, improve the overall operational efficiency of conveyor agents, and reduce their energy consumption.

A multi-agent system simulation of job shop scheduling with human consideration: A comparative analysis of AGVs and AIVs

A multi-agent system simulation of job shop scheduling with human consideration: A comparative analysis of AGVs and AIVs

Double weighted combat data quality evaluation method based on CVF optimized FAHP

During multi-agent combat simulation exercises, accurately assessing the quality of collected combat data is a critical step. Addressing the current issue of low accuracy in combat data quality evaluation, which fails to effectively support simulation exercises, this paper proposes a Double-Weighted FAHP optimized by CVF (comparative value function) method for assessing combat data quality. First, a three-tiered evaluation framework for combat data quality indicators is established, with threshold values determined for each indicator. The weights obtained from the FAHP method are optimized using the Satisfaction Consistency Approach to derive the first-tier weights. Subsequently, the CVF is constructed to obtain the second-tier weights. The double-weighted evaluation theory combines these two tiers of weights to produce the final assessment. Analysis of the experimental results indicates that the proposed method reduces the mean squared error to 5.35 when compared to results obtained using FAHP, interval intuitionistic fuzzy methods, and artificial neural networks, bringing it closer to actual standard values. This method provides a more accurate evaluation of the quality of multi-agent combat data, offering robust data support for future combat simulation exercises and military drills.

A simulation model of negotiations on affordable housing provision

ABSTRACT In several planning systems, the provision of affordable housing is negotiated between local authorities and private developers. Those negotiations are not often accessible to students and researchers for understanding and analysis. This article presents a multi-agent simulation model of the negotiations on affordable housing in England. The model is designed to help students and researchers generate hypotheses about the impact of housing markets, local policies and negotiation behaviors on the negotiation outcomes, and consequently, the provision of affordable housing. We conducted a thought experiment using the model. To demonstrate reliability, we compared the outputs against overall trends of affordable housing provisions in England and a previous empirical study of simple negotiations. The results helped in generating four hypotheses. Among them, interestingly, the model hypothesized that there is a range of possible outcomes for each housing market, and negotiation skills can only cause variations within that range. The experiment demonstrated the model’s applicability as a tool for generating hypotheses which can open doors for future exploration, hence supporting housing education and research.

Agent-based model for forecasting the impact of the population life quality on migration movement in the context of the Russian Federation federal districts.

The Russian Federation is characterized by an extremely uneven distribution of the population across the country, which contributes to the asymmetry of economic and socio-demographic development of the regions, a shortage of qualified specialists for the development of the resources of Siberia and the Far East, and an increase in global risks in general. In this regard, the use of modern management technologies, in particular, multi-agent simulation modeling, to support decision-making on managing migration processes becomes relevant. Since the main incentive for active citizens to change their place of residence is investing in the development of the region and providing the necessary conditions for a comfortable life, the purpose of the study is to develop an agent-based model for forecasting the impact of the population life quality on migration flows between the federal districts of the Russian Federation. One of the tasks solved using the model is to track the direction of migrant movement relative to the Republic of Bashkortostan when changing the controlled parameters. The simulation model was designed using modern CASE tools; UML diagrams and a mnemonic diagram of the decision-making support process for managing the demographic development of the region were built in the course of the work. Scenario experiments were conducted to predict changes in the population size in the study areas. Within the framework of the research, the authors applied the object-oriented methodology of simulation model design, agent-based approach for its implementation, as well as an agent-oriented approach for its implementation and statistical analysis methods when setting up experiments. The toolkit developed as a result of the study can be used by the representatives of executive authorities and government officials to develop a balanced resettlement policy, assess the possibility and conditions for developing regions of the Russian Federation with low population density.

Methodology for Optimizing Charging Infrastructure Distribution for Long-Haul Freight Traffic based on Multi-Agent Simulation and Evolutionary Bi-Objective Optimization

Methodology for Optimizing Charging Infrastructure Distribution for Long-Haul Freight Traffic based on Multi-Agent Simulation and Evolutionary Bi-Objective Optimization

Evaluation of prognosis solutions for wind turbine using multi agent simulation of maintenance business

Evaluation of prognosis solutions for wind turbine using multi agent simulation of maintenance business